Machine learning has generally been thought of and attempted to be implemented in the context of artificial intelligence. Artificial intelligence depends on algorithmic solutions (e.g., a computer program) to replicate particular human acts and/or behaviors. The study of neural networks is a sub-area of artificial intelligence which attempts to mimic certain human brain behavior by using individual processing elements that are interconnected by adjustable connections.
In human cognition, perception and understanding of phenomena happen over time and space. Both time and space are used to generalize the things that we perceive and sense. For example, we learn things have a common cause based on “temporal adjacency”, meaning that two phenomena around the same time. Spatial similarity refers to our ability to distinguish an objects are the same or belong to the same group of objects based on perceived similarity.
Spatial and temporal perception work synergistically in cognition. The inability to both spatial similarity and temporal events has been a fundamental limitation in most traditional machine learning models.